Statistics and geometry of orientation selectivity in primary visual cortex.

Sadeh S, Rotter S - Biol Cybern (2013)

Bottom Line:
In macaques and cats, for example, preferred orientations of neurons are organized in a specific pattern, where cells with similar selectivity are clustered in iso-orientation domains.However, the map is not always continuous, and there are pinwheel-like singularities around which all orientations are arranged in an orderly fashion.Without any additional assumptions, we further show that the pattern of ocular dominance columns is inherently connected to the spatial pattern of orientation.

ABSTRACTOrientation maps are a prominent feature of the primary visual cortex of higher mammals. In macaques and cats, for example, preferred orientations of neurons are organized in a specific pattern, where cells with similar selectivity are clustered in iso-orientation domains. However, the map is not always continuous, and there are pinwheel-like singularities around which all orientations are arranged in an orderly fashion. Although subject of intense investigation for half a century now, it is still not entirely clear how these maps emerge and what function they might serve. Here, we suggest a new model of orientation selectivity that combines the geometry and statistics of clustered thalamocortical afferents to explain the emergence of orientation maps. We show that the model can generate spatial patterns of orientation selectivity closely resembling the maps found in cats or monkeys. Without any additional assumptions, we further show that the pattern of ocular dominance columns is inherently connected to the spatial pattern of orientation.

Fig5: Multi-columnar receptive fields. a A hexagonal grid of columns described in Fig. 2. Shown is the aggregate receptive field of the center column. Other columns have the same columnar receptive fields, centered at the center of columns (small white circles). b Receptive field of a neuron located at the center of a central column. c Receptive field of a neuron located between the central column and one of the neighboring columns. d Tuning curve of neuronal input. The receptive field of the neuron is stimulated with drifting gratings of 18 different orientations (shown on the x-axis). The temporal modulation of the response for each orientation is shown on the y-axis

Mentions:
The simplification introduced in the previous section allows us to go beyond one column in our model and investigate interactions between columns. Let us first consider a hexagonal grid of such columns (Braitenberg 1985), as shown in Fig. 5a. The receptive field of each cortical neuron in this columnar structure is given by a weighted sum of all the columnar receptive fields. The corresponding weights come from a Gaussian function of the distance to the center of each column.Fig. 5

Fig5: Multi-columnar receptive fields. a A hexagonal grid of columns described in Fig. 2. Shown is the aggregate receptive field of the center column. Other columns have the same columnar receptive fields, centered at the center of columns (small white circles). b Receptive field of a neuron located at the center of a central column. c Receptive field of a neuron located between the central column and one of the neighboring columns. d Tuning curve of neuronal input. The receptive field of the neuron is stimulated with drifting gratings of 18 different orientations (shown on the x-axis). The temporal modulation of the response for each orientation is shown on the y-axis

Mentions:
The simplification introduced in the previous section allows us to go beyond one column in our model and investigate interactions between columns. Let us first consider a hexagonal grid of such columns (Braitenberg 1985), as shown in Fig. 5a. The receptive field of each cortical neuron in this columnar structure is given by a weighted sum of all the columnar receptive fields. The corresponding weights come from a Gaussian function of the distance to the center of each column.Fig. 5

Bottom Line:
In macaques and cats, for example, preferred orientations of neurons are organized in a specific pattern, where cells with similar selectivity are clustered in iso-orientation domains.However, the map is not always continuous, and there are pinwheel-like singularities around which all orientations are arranged in an orderly fashion.Without any additional assumptions, we further show that the pattern of ocular dominance columns is inherently connected to the spatial pattern of orientation.

ABSTRACTOrientation maps are a prominent feature of the primary visual cortex of higher mammals. In macaques and cats, for example, preferred orientations of neurons are organized in a specific pattern, where cells with similar selectivity are clustered in iso-orientation domains. However, the map is not always continuous, and there are pinwheel-like singularities around which all orientations are arranged in an orderly fashion. Although subject of intense investigation for half a century now, it is still not entirely clear how these maps emerge and what function they might serve. Here, we suggest a new model of orientation selectivity that combines the geometry and statistics of clustered thalamocortical afferents to explain the emergence of orientation maps. We show that the model can generate spatial patterns of orientation selectivity closely resembling the maps found in cats or monkeys. Without any additional assumptions, we further show that the pattern of ocular dominance columns is inherently connected to the spatial pattern of orientation.